Triple
T19764
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chevalier de la Légion d'honneur |
E393
|
entity |
| Predicate | orderType |
P1802
|
FINISHED |
| Object | order of merit |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: order of merit | Statement: [Chevalier de la Légion d'honneur, orderType, order of merit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: orderType Context triple: [Chevalier de la Légion d'honneur, orderType, order of merit]
-
A.
order
Indicates that one entity requests, arranges, or directs that another entity provide a good, service, or action, typically in a specified sequence or priority.
-
B.
hasOrder
Indicates that one entity possesses, is associated with, or is characterized by a specific order, sequence, or arrangement relative to others.
-
C.
orderInUnion
Indicates the relative position or sequence of an entity within a union or ordered collection of entities.
-
D.
orderInOffice
Indicates that one entity holds a specific sequential position or rank within a defined term or period of holding an office or official role.
-
E.
operationType
Indicates the specific kind of operation or action being performed or recorded in the relationship between entities.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a240778d288190815c0052ebbbcc91 |
completed | Feb. 28, 2026, 1:10 a.m. |
| NER | Named-entity recognition | batch_69a24703cb988190ad2bc181d27829e4 |
completed | Feb. 28, 2026, 1:38 a.m. |
| PD | Predicate disambiguation | batch_69a24650f1f0819081e638fafd18d687 |
completed | Feb. 28, 2026, 1:35 a.m. |
| PDg | Predicate description generation | batch_69a24702d4988190a54a4e578b7c919e |
completed | Feb. 28, 2026, 1:38 a.m. |
Created at: Feb. 28, 2026, 1:14 a.m.